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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 16 Documents
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Search results for , issue "Vol 8, No 2 (2023)" : 16 Documents clear
Tabel Partisi Pada STARS: Konsep Dan Evaluasi (Studi Kasus STARS UKSW) Boymau, Infraim Oktofianus; Tanaem, Penidas Fiodinggo; Tanaamah, Andeka Rocky
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.4753

Abstract

Database performance is one of the main components in supporting the sustainability of a system, in this case, STARS. In the system context, data will usually be collected into a database. Tied to the data collection process, this really affects the performance of a system as a whole, in this case when executing a query to get a return of the execution results, because the performance of the database itself will be affected by the amount of data available. One way to improve the performance of the database is to use the partition table concept. Thus, in this research a design and evaluation of the partition table will be carried out which will then be applied to the SWCU STARS database. This research focuses more on the use of vertical partitions and list partitions by utilizing PostgreSQL version 14. The stages used in this study. These stages include data collection, partition design, technical partition, testing and implementation. The results of this study indicate that partition tables have better performance than non-partition tables. Judging from some of the sql syntax, namely update, delete and select, while insert has poor performance for partition tables compared to non-partition tables
Pengenalan Alfabet SIBI Menggunakan Convolutional Neural Network sebagai Media Pembelajaran Bagi Masyarakat Umum Fadhilah, Zahrah; Marpaung, Noveri Lysbetti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5221

Abstract

SIBI is one of the Sign Languages used in Indonesia and has been widely used in the community, especially the school (SLB). Communication limitations of the deaf and speech community cause limited communication with the general public, especially many general public who do not know Sign Language or SIBI. For this reason, this research was conducted in order to become a learning media for the general public in recognizing the SIBI alphabet so that it can support communication with the deaf and speech community. This research was conducted to become a medium that can be used as a learning medium in the introduction of the SIBI alphabet. The method used in this research is CNN. CNN is used because it is a deep learning method that has the most significant results in image recognition. The data used is 2,600 images which are divided into 80% training data and 20% validation data. Training was done ten times by comparing the parameters that produce the best accuracy. The parameters used are batch size and epoch. From ten trials, the best accuracy is obtained using batch size 8 and epoch 50. The best accuracy produced is 85% training accuracy and 87% validation accuracy.
Implementasi Aplikasi Sentimen Pada Data Twitter Jelang Pemilu 2024 Humam, Choirul; Laksito, Arif Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5051

Abstract

Elections are one of the most important democratic processes, giving citizens the right to choose their leaders. In today's digital era, social media is an increasingly important information source influencing public perception. Twitter has been a social media from the past until now that still exists in finding information. Tweets are one of the most frequently used services to express opinions or opinions to the public. Sentiment analysis as an application of Natural Language Processing (NLP) is helpful in understanding public opinion towards prospective leaders and issues discussed during election campaigns. The motivation for this study is to conduct text classification using a deep learning model called LSTM and to compare the use of oversampling and non-oversampling methods. This research started by collecting datasets from Twitter, labelling, pre-processing, creating and evaluating the model, and implementing it into the web application. The experiment showed that the random oversampling technique gets more significant accuracy than non-oversampling. Random oversampling produces an accuracy of 0.82 at epoch 25, while non-oversampling reaches an accuracy of 0.61 at epoch 50
Deep Learning untuk Identifikasi Daun Tanaman Obat Menggunakan Transfer Learning MobileNetV2 Hendri Butar-Butar, Rio Juan; Marpaung, Noveri Lysbetti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5217

Abstract

Medicinal plants are plants used as alternative medicines for healing or preventing various diseases due to their active substances. The utilization of medicinal plants in Indonesia has been widespread among the community since ancient times and is a heritage passed down from ancestors. Medicinal plants have leaf structures that are almost similar between one plant and another, which can lead to confusion for some people and require precision in identifying the leaves of medicinal plants. Incorrect identification can have negative consequences for the users. In recent years, deep learning has been used to identify objects because of its ability to interpret images. This study used a transfer learning method to identify medicinal plants. Transfer learning utilizes a pre-trained model to learn and perform new tasks, making it suitable for smaller datasets. The pre-trained model used in this study is MobileNetV2. MobileNetV2 has a lightweight architecture and high accuracy. Fine-tuning techniques were applied in this study to improve the model's performance. Several experiments were conducted with parameters such as epochs and fine-tuning layers to obtain the best results. The research yielded a training accuracy of 97%, validation accuracy of 96%, and testing accuracy of 93%.
Klasifikasi Jamur Berdasarkan Genus Dengan Menggunakan Metode CNN Rahmadhani, Ummi Sri; Marpaung, Noveri Lysbetti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5229

Abstract

Mushrooms are plants that do not have true roots and leaves. There are many types of mushrooms that have been identified worldwide, with various shapes, sizes, and colors. Mushrooms have many benefits in the fields of economy, health, and others. One of the benefits of mushrooms is as a food source in Indonesia, but not all types can be consumed. To identify mushroom species, the concepts of Genus and species can be used. The concept of Genus is considered easier because it groups mushroom types based on similar morphological characteristics. Therefore, a model is needed to classify mushrooms based on consumable and toxic genera. The method used in this research is Convolution Neural Network (CNN) due to its good predictive results in image recognition. The model in the research utilizes three convolution layers, three MaxPooling layers, and two dropout layers. The use of dropout aims to reduce overfitting in the model. The research uses a dataset of 1200 images with a training and testing data ratio of 70:30, resulting in 840 training data and 360 testing data. The best accuracy achieved by this model is 89% for training and 82% for validation. Therefore, it can be concluded that the model is able to classify mushrooms based on Genus using the CNN method
Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti; Ahmed, Foez
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5207

Abstract

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.

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